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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        About MultiQC

        This report was generated using MultiQC, version 1.29

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-06-04, 08:58 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data

        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant.URL: https://github.com/bigbio/pmultiqc


        Summary and HeatMap

        Summary Table

        This table shows the quantms pipeline summary statistics.
        This table shows the quantms pipeline summary statistics.
        Showing 1/1 rows.
        #Peptides Quantified#Proteins Quantified
        3725
        765

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result.
        Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline, and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
        Showing 12/12 rows and 4/4 columns.
        Sample Name#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
        20221028_FL_Lu_SV_Set12_A1
        2871
        2871
        402
        793
        20221028_FL_Lu_SV_Set12_A2
        2877
        2877
        393
        786
        20221028_FL_Lu_SV_Set12_A3
        2513
        2513
        336
        688
        20221028_FL_Lu_SV_Set12_A4
        3001
        3001
        411
        835
        20221028_FL_Lu_SV_Set12_A5
        2941
        2941
        413
        818
        20221028_FL_Lu_SV_Set12_A6
        3020
        3020
        419
        837
        20221028_FL_Lu_SV_Set12_B1
        2641
        2641
        350
        759
        20221028_FL_Lu_SV_Set12_B2
        2766
        2766
        361
        805
        20221028_FL_Lu_SV_Set12_B3
        3043
        3043
        408
        888
        20221028_FL_Lu_SV_Set12_B4
        2887
        2887
        387
        832
        20221028_FL_Lu_SV_Set12_B5
        2336
        2336
        313
        669
        20221028_FL_Lu_SV_Set12_B6
        2517
        2517
        340
        744

        Identification Summary

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in quantms pipeline final result
        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.
        Created with MultiQC

        MS1 Analysis

        Total Ion Chromatograms

        MS1 quality control information extracted from the spectrum files.
        This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples. The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point. Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column. Key aspects to assess include: * Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility. * Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance. * Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity. * Chromatographic resolution: Sharp, well-separated peaks indicate effective separation. * Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues. Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.
        Created with MultiQC

        MS1 Base Peak Chromatograms

        MS1 base peak chromatograms extracted from the spectrum files.
        The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed intensity of all ions, the BPC highlights the strongest signals, providing better visualization of compounds with high abundance while reducing baseline noise. This makes it particularly useful for identifying major components in complex samples, monitoring dominant species, and providing clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across samples allows you to evaluate consistency in the detection of high-abundance compounds and can reveal significant variations in sample composition or instrument performance.
        Created with MultiQC

        MS1 Peaks

        MS1 Peaks from the spectrum files
        This plot shows the number of peaks detected in MS1 scans over the course of each sample run. The x-axis represents retention time (in minutes), while the y-axis displays the number of distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral complexity and provides insight into instrument performance during the LC-MS analysis. Key aspects to consider include: * Overall pattern: Peak counts typically increase during the elution of complex mixtures and decrease during column washing or re-equilibration phases. * Peak density: Higher counts suggest more complex spectra, potentially indicating a greater number of compounds present at that time point." * Peak Consistency across samples: Similar profiles among replicates or related samples indicate good analytical reproducibility. * Sudden drops: Abrupt decreases in peak count may point to transient ionization issues, spray instability, or chromatographic disruptions. * Baseline values: The minimum peak count observed reflects the level of background noise or instrument sensitivity in the absence of eluting compounds. Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram (BPC) data, offering an additional layer of quality control related to signal complexity, instrument stability, and sample composition.
        Created with MultiQC

        General stats for MS1 information

        General stats for MS1 information extracted from the spectrum files.
        This table presents general statistics for MS1 information extracted from mass spectrometry data files." It displays MS runs with their acquisition dates and times. For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities). These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2), allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation, instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.
        Showing 12/12 rows and 3/3 columns.
        FileAcquisition Date Timelog10(Total Current)log10(Scan Current)
        20221028_FL_Lu_SV_Set12_A1
        2022-10-29 22:11:31
        11.7957
        11.3888
        20221028_FL_Lu_SV_Set12_A2
        2022-10-30 01:33:52
        11.8052
        34.2775
        20221028_FL_Lu_SV_Set12_A3
        2022-10-30 13:47:37
        11.7305
        11.3256
        20221028_FL_Lu_SV_Set12_A4
        2022-10-30 06:40:43
        11.8220
        11.4029
        20221028_FL_Lu_SV_Set12_A5
        2022-10-30 09:03:01
        11.8009
        11.3846
        20221028_FL_Lu_SV_Set12_A6
        2022-10-30 01:56:09
        11.8099
        11.4013
        20221028_FL_Lu_SV_Set12_B1
        2022-10-29 12:42:17
        11.7506
        11.3651
        20221028_FL_Lu_SV_Set12_B2
        2022-10-30 11:25:17
        11.8033
        11.3784
        20221028_FL_Lu_SV_Set12_B3
        2022-10-29 15:04:34
        11.8335
        11.4320
        20221028_FL_Lu_SV_Set12_B4
        2022-10-30 04:18:27
        11.8097
        11.4051
        20221028_FL_Lu_SV_Set12_B5
        2022-10-29 17:26:54
        11.6971
        11.3046
        20221028_FL_Lu_SV_Set12_B6
        2022-10-29 19:49:11
        11.7172
        11.3146

        MS2 and Spectral Stats

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment.
        This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).
        Created with MultiQC

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.
        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.
        Created with MultiQC

        Distribution of Precursor Charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.
        Created with MultiQC